aceApplications and performance of imaging artificial intelligence in detecting and staging avascular necrosis of the femoral head: a systematic review and meta-analysis
Objective To evaluate the diagnostic accuracy of imaging artificial intelligence (AI) models for detecting avascular necrosis of the femoral head (AVNFH) and to examine whether diagnostic performance differs according to imaging modality, disease-stage focus, and validation strategy. Materials and methods In accordance with PRISMA-DTA, we conducted a systematic search in four main databases. Two independent reviewers evaluated the studies, extracted relevant data, and assessed the quality following the QUADAS-2…
Imaging AI models achieved high pooled diagnostic accuracy for detecting avascular necrosis of the femoral head in research datasets.
Risk of bias, heterogeneity, and limited external testing restrict confidence that reported accuracy will translate to routine clinical use.
Confidence in clinical applicability is limited by risk of bias, between-study heterogeneity, and sparse external testing, with only five external test-set estimates in primary analysis.
Evidence
- Peer-reviewedSkeletal Radiology2026-10-04
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Truvace Impact Record TRV-2026-1280, v1: “Applications and performance of imaging artificial intelligence in detecting and staging avascular necrosis of the femoral head: a systematic review and meta-analysis.” Truvace, 2026-10-05. /record/TRV-2026-1280 (accessed at citation time). sha256 e5cea0f3f722c1a9…
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